Surface ECG spectral analysis to predict atrial fibrillation catheter ablation long-term outcome

Catheter ablation (CA) has emerged recently as an effective tool to treat atrial fibrillation (AF), which is today the most common cardiac arrhythmia. However, the long-term AF recurrence rate is about 50% for patients in persistent AF. As a consequence the optimal selection of patients for the procedure remains as an interesting clinical challenge. To this respect, the dominant atrial frequency (DAF) analysis from the surface ECG has been widely studied in recent years, reporting promising results. In the present work two additional spectral metrics complementing the information provided by the DAF, such as its 3 dB bandwidth (BW) and the median frequency (MF), are for the first time studied. Results provided no statistically significant differences for the DAF as well as the BW between patients with and without freedom from AF after a mean follow-up of 12±7 months. In contrast, a statistically significant greater MF was observed in patients maintaining sinus rhythm (6.42±0.17 Hz) than for those who relapsed to AF during the folow-up (6.03±0.30 Hz). Moreover, the MF provided sensitivity, specificity and accuracy values of 83.33%, 100% and 91.67%, respectively. Hence, the MF could be considered as a more promising harbinger of long-term CA outcome than the DAF. Further studies in wider databases should validate this preliminar finding.

[1]  J. J. Rieta,et al.  Adaptive singular value cancelation of ventricular activity in single-lead atrial fibrillation electrocardiograms , 2008, Physiological measurement.

[2]  Robert Ploutz-Snyder,et al.  Real-time dominant frequency mapping and ablation of dominant frequency sites in atrial fibrillation with left-to-right frequency gradients predicts long-term maintenance of sinus rhythm. , 2009, Heart rhythm.

[3]  Srikant Duggirala,et al.  Atrial fibrillation ablation: indications, emerging techniques, and follow-up. , 2015, Progress in cardiovascular diseases.

[4]  M. Ezekowitz,et al.  2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines and the Heart Rhythm Society. , 2014, Circulation.

[5]  M. Zoni-Berisso,et al.  Epidemiology of atrial fibrillation: European perspective , 2014, Clinical epidemiology.

[6]  Robby Nieuwlaat,et al.  Progression from paroxysmal to persistent atrial fibrillation clinical correlates and prognosis. , 2010, Journal of the American College of Cardiology.

[7]  Federica Censi,et al.  Atrial fibrillation and the 4P medicine. , 2013, Annali dell'Istituto superiore di sanita.

[8]  A. Camm,et al.  Classification of Atrial Fibrillation , 1997, The American journal of cardiology.

[9]  Prashanthan Sanders,et al.  Long‐term Outcomes of Catheter Ablation of Atrial Fibrillation: A Systematic Review and Meta‐analysis , 2013, Journal of the American Heart Association.

[10]  D. Theuns,et al.  Clinical outcome of ablation for long-standing persistent atrial fibrillation with or without defragmentation , 2013, Netherlands Heart Journal.

[11]  Nadine Eberhardt,et al.  Bioelectrical Signal Processing In Cardiac And Neurological Applications , 2016 .

[12]  P. Kirchhof,et al.  Pathophysiological mechanisms of atrial fibrillation: a translational appraisal. , 2011, Physiological reviews.

[13]  Atul Verma,et al.  Atrial fibrillation termination as a procedural endpoint during ablation in long-standing persistent atrial fibrillation. , 2010, Heart rhythm.

[14]  Haitham M. Al-Angari,et al.  Atrial fibrillation and waveform characterization. A time domain perspective in the surface ECG. , 2006, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[15]  Kentaro Yoshida,et al.  Catheter ablation of atrial fibrillation: Past, present, and future directions , 2012 .

[16]  Frank Bogun,et al.  Effects of two different catheter ablation techniques on spectral characteristics of atrial fibrillation. , 2006, Journal of the American College of Cardiology.

[17]  Roberto Hornero,et al.  Spectral and Nonlinear Analyses of MEG Background Activity in Patients With Alzheimer's Disease , 2008, IEEE Transactions on Biomedical Engineering.

[18]  Philip Langley,et al.  Characteristics of atrial fibrillation cycle length predict restoration of sinus rhythm by catheter ablation. , 2013, Heart rhythm.

[19]  Raúl Alcaraz,et al.  Time and frequency recurrence analysis of persistent atrial fibrillation after electrical cardioversion , 2009, Physiological measurement.

[20]  Haitham M. Al-Angari,et al.  Atrial fibrillation and waveform characterization , 2006, IEEE Engineering in Medicine and Biology Magazine.

[21]  Leif Sörnmo,et al.  Sequential characterization of atrial tachyarrhythmias based on ECG time-frequency analysis , 2004, IEEE Transactions on Biomedical Engineering.